A Connectionist Structured Object Recognizer For Noisy Images

Oleg G. Jakubowicz

doi:10.1117/12.946993

29 March 1988 A Connectionist Structured Object Recognizer For Noisy Images

Oleg G. Jakubowicz

Proceedings Volume 0937, Applications of Artificial Intelligence VI; (1988) https://doi.org/10.1117/12.946993
Event: 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, 1988, Orlando, FL, United States

Abstract

The vision model described in this paper utilizes a competitive winner-take-all network with several biological inspired levels similar to the Neocognitron and the work of Frohn, Geiger and Singer with improvements. Their models are simple object detectors and they incorporate feature detector levels. In the current system many of these simple object detectors are used and each one presents spatial information that is used by the top complex structured object detector level. This top level is inspired from Patrick Winston's and others' A.I. work on learning and matching structural descriptions of complex objects. The synaptic connections at this level contain relational information such as right-of, left-of, in-front-of, must-be and must-not. The winner-take-all characteristic of each level allows even partially obscured objects to be recognized. Forbidden properties of an object can be implemented by inhibitory connections while essential properties are implemented by excitatory connections. In this way a small number of missing essential properties does not automatically rule out the recognition of an object while present forbidden properties quickly rule it out.

Citation Download Citation

Oleg G. Jakubowicz "A Connectionist Structured Object Recognizer For Noisy Images", Proc. SPIE 0937, Applications of Artificial Intelligence VI, (29 March 1988); https://doi.org/10.1117/12.946993

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